Overview

Brought to you by YData

Dataset statistics

Number of variables10
Number of observations768
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory60.1 KiB
Average record size in memory80.2 B

Variable types

Numeric6
Categorical4

Alerts

Cooling_Load is highly overall correlated with Heating_Load and 4 other fieldsHigh correlation
Glazing_Area is highly overall correlated with Glazing_Area_Distribution and 1 other fieldsHigh correlation
Glazing_Area_Distribution is highly overall correlated with Glazing_AreaHigh correlation
Heating_Load is highly overall correlated with Cooling_Load and 5 other fieldsHigh correlation
Overall_Height is highly overall correlated with Cooling_Load and 5 other fieldsHigh correlation
Relative_Compactness is highly overall correlated with Cooling_Load and 4 other fieldsHigh correlation
Roof_Area is highly overall correlated with Cooling_Load and 5 other fieldsHigh correlation
Surface_Area is highly overall correlated with Cooling_Load and 4 other fieldsHigh correlation
Wall_Area is highly overall correlated with Overall_Height and 1 other fieldsHigh correlation
Overall_Height is uniformly distributedUniform
Orientation is uniformly distributedUniform
Glazing_Area_Distribution has 48 (6.2%) zerosZeros

Reproduction

Analysis started2024-09-10 14:34:59.116722
Analysis finished2024-09-10 14:35:11.214094
Duration12.1 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

Relative_Compactness
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.76416667
Minimum0.62
Maximum0.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2024-09-10T20:05:11.446842image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.62
5-th percentile0.62
Q10.6825
median0.75
Q30.83
95-th percentile0.98
Maximum0.98
Range0.36
Interquartile range (IQR)0.1475

Descriptive statistics

Standard deviation0.10577748
Coefficient of variation (CV)0.138422
Kurtosis-0.70656754
Mean0.76416667
Median Absolute Deviation (MAD)0.08
Skewness0.49551251
Sum586.88
Variance0.011188874
MonotonicityNot monotonic
2024-09-10T20:05:11.786540image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0.98 64
8.3%
0.9 64
8.3%
0.86 64
8.3%
0.82 64
8.3%
0.79 64
8.3%
0.76 64
8.3%
0.74 64
8.3%
0.71 64
8.3%
0.69 64
8.3%
0.66 64
8.3%
Other values (2) 128
16.7%
ValueCountFrequency (%)
0.62 64
8.3%
0.64 64
8.3%
0.66 64
8.3%
0.69 64
8.3%
0.71 64
8.3%
0.74 64
8.3%
0.76 64
8.3%
0.79 64
8.3%
0.82 64
8.3%
0.86 64
8.3%
ValueCountFrequency (%)
0.98 64
8.3%
0.9 64
8.3%
0.86 64
8.3%
0.82 64
8.3%
0.79 64
8.3%
0.76 64
8.3%
0.74 64
8.3%
0.71 64
8.3%
0.69 64
8.3%
0.66 64
8.3%

Surface_Area
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean671.70833
Minimum514.5
Maximum808.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2024-09-10T20:05:12.121051image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum514.5
5-th percentile514.5
Q1606.375
median673.75
Q3741.125
95-th percentile808.5
Maximum808.5
Range294
Interquartile range (IQR)134.75

Descriptive statistics

Standard deviation88.086116
Coefficient of variation (CV)0.13113745
Kurtosis-1.0594542
Mean671.70833
Median Absolute Deviation (MAD)73.5
Skewness-0.12513088
Sum515872
Variance7759.1638
MonotonicityNot monotonic
2024-09-10T20:05:12.456005image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
514.5 64
8.3%
563.5 64
8.3%
588 64
8.3%
612.5 64
8.3%
637 64
8.3%
661.5 64
8.3%
686 64
8.3%
710.5 64
8.3%
735 64
8.3%
759.5 64
8.3%
Other values (2) 128
16.7%
ValueCountFrequency (%)
514.5 64
8.3%
563.5 64
8.3%
588 64
8.3%
612.5 64
8.3%
637 64
8.3%
661.5 64
8.3%
686 64
8.3%
710.5 64
8.3%
735 64
8.3%
759.5 64
8.3%
ValueCountFrequency (%)
808.5 64
8.3%
784 64
8.3%
759.5 64
8.3%
735 64
8.3%
710.5 64
8.3%
686 64
8.3%
661.5 64
8.3%
637 64
8.3%
612.5 64
8.3%
588 64
8.3%

Wall_Area
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean318.5
Minimum245
Maximum416.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2024-09-10T20:05:12.786422image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum245
5-th percentile245
Q1294
median318.5
Q3343
95-th percentile416.5
Maximum416.5
Range171.5
Interquartile range (IQR)49

Descriptive statistics

Standard deviation43.626481
Coefficient of variation (CV)0.13697482
Kurtosis0.11659327
Mean318.5
Median Absolute Deviation (MAD)24.5
Skewness0.53341749
Sum244608
Variance1903.2699
MonotonicityNot monotonic
2024-09-10T20:05:13.139232image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
294 192
25.0%
318.5 192
25.0%
343 128
16.7%
416.5 64
 
8.3%
245 64
 
8.3%
269.5 64
 
8.3%
367.5 64
 
8.3%
ValueCountFrequency (%)
245 64
 
8.3%
269.5 64
 
8.3%
294 192
25.0%
318.5 192
25.0%
343 128
16.7%
367.5 64
 
8.3%
416.5 64
 
8.3%
ValueCountFrequency (%)
416.5 64
 
8.3%
367.5 64
 
8.3%
343 128
16.7%
318.5 192
25.0%
294 192
25.0%
269.5 64
 
8.3%
245 64
 
8.3%

Roof_Area
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size40.7 KiB
220.5
384 
147.0
192 
122.5
128 
110.25
64 

Length

Max length6
Median length5
Mean length5.0833333
Min length5

Characters and Unicode

Total characters3904
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row110.25
2nd row110.25
3rd row110.25
4th row110.25
5th row122.5

Common Values

ValueCountFrequency (%)
220.5 384
50.0%
147.0 192
25.0%
122.5 128
 
16.7%
110.25 64
 
8.3%

Length

2024-09-10T20:05:13.497599image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-10T20:05:13.845676image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
220.5 384
50.0%
147.0 192
25.0%
122.5 128
 
16.7%
110.25 64
 
8.3%

Most occurring characters

ValueCountFrequency (%)
2 1088
27.9%
. 768
19.7%
0 640
16.4%
5 576
14.8%
1 448
11.5%
4 192
 
4.9%
7 192
 
4.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3904
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 1088
27.9%
. 768
19.7%
0 640
16.4%
5 576
14.8%
1 448
11.5%
4 192
 
4.9%
7 192
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3904
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 1088
27.9%
. 768
19.7%
0 640
16.4%
5 576
14.8%
1 448
11.5%
4 192
 
4.9%
7 192
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3904
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 1088
27.9%
. 768
19.7%
0 640
16.4%
5 576
14.8%
1 448
11.5%
4 192
 
4.9%
7 192
 
4.9%

Overall_Height
Categorical

HIGH CORRELATION  UNIFORM 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size39.1 KiB
7.0
384 
3.5
384 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2304
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row7.0
2nd row7.0
3rd row7.0
4th row7.0
5th row7.0

Common Values

ValueCountFrequency (%)
7.0 384
50.0%
3.5 384
50.0%

Length

2024-09-10T20:05:14.187745image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-10T20:05:14.456872image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
7.0 384
50.0%
3.5 384
50.0%

Most occurring characters

ValueCountFrequency (%)
. 768
33.3%
7 384
16.7%
0 384
16.7%
3 384
16.7%
5 384
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2304
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 768
33.3%
7 384
16.7%
0 384
16.7%
3 384
16.7%
5 384
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2304
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 768
33.3%
7 384
16.7%
0 384
16.7%
3 384
16.7%
5 384
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2304
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 768
33.3%
7 384
16.7%
0 384
16.7%
3 384
16.7%
5 384
16.7%

Orientation
Categorical

UNIFORM 

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size37.6 KiB
2
192 
3
192 
4
192 
5
192 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters768
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row4
4th row5
5th row2

Common Values

ValueCountFrequency (%)
2 192
25.0%
3 192
25.0%
4 192
25.0%
5 192
25.0%

Length

2024-09-10T20:05:14.727745image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-10T20:05:15.036866image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2 192
25.0%
3 192
25.0%
4 192
25.0%
5 192
25.0%

Most occurring characters

ValueCountFrequency (%)
2 192
25.0%
3 192
25.0%
4 192
25.0%
5 192
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 768
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 192
25.0%
3 192
25.0%
4 192
25.0%
5 192
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 768
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 192
25.0%
3 192
25.0%
4 192
25.0%
5 192
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 768
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 192
25.0%
3 192
25.0%
4 192
25.0%
5 192
25.0%

Glazing_Area
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size39.4 KiB
0.1
240 
0.25
240 
0.4
240 
0.0
48 

Length

Max length4
Median length3
Mean length3.3125
Min length3

Characters and Unicode

Total characters2544
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.1 240
31.2%
0.25 240
31.2%
0.4 240
31.2%
0.0 48
 
6.2%

Length

2024-09-10T20:05:15.407857image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-10T20:05:15.722103image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.1 240
31.2%
0.25 240
31.2%
0.4 240
31.2%
0.0 48
 
6.2%

Most occurring characters

ValueCountFrequency (%)
0 816
32.1%
. 768
30.2%
1 240
 
9.4%
2 240
 
9.4%
5 240
 
9.4%
4 240
 
9.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2544
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 816
32.1%
. 768
30.2%
1 240
 
9.4%
2 240
 
9.4%
5 240
 
9.4%
4 240
 
9.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2544
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 816
32.1%
. 768
30.2%
1 240
 
9.4%
2 240
 
9.4%
5 240
 
9.4%
4 240
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2544
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 816
32.1%
. 768
30.2%
1 240
 
9.4%
2 240
 
9.4%
5 240
 
9.4%
4 240
 
9.4%

Glazing_Area_Distribution
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8125
Minimum0
Maximum5
Zeros48
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2024-09-10T20:05:16.017909image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.75
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2.25

Descriptive statistics

Standard deviation1.5509597
Coefficient of variation (CV)0.55145233
Kurtosis-1.1487088
Mean2.8125
Median Absolute Deviation (MAD)1
Skewness-0.088689175
Sum2160
Variance2.4054759
MonotonicityNot monotonic
2024-09-10T20:05:16.308154image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 144
18.8%
2 144
18.8%
3 144
18.8%
4 144
18.8%
5 144
18.8%
0 48
 
6.2%
ValueCountFrequency (%)
0 48
 
6.2%
1 144
18.8%
2 144
18.8%
3 144
18.8%
4 144
18.8%
5 144
18.8%
ValueCountFrequency (%)
5 144
18.8%
4 144
18.8%
3 144
18.8%
2 144
18.8%
1 144
18.8%
0 48
 
6.2%

Heating_Load
Real number (ℝ)

HIGH CORRELATION 

Distinct587
Distinct (%)76.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.307195
Minimum6.01
Maximum43.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2024-09-10T20:05:16.597822image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum6.01
5-th percentile10.4635
Q112.9925
median18.95
Q331.6675
95-th percentile39.86
Maximum43.1
Range37.09
Interquartile range (IQR)18.675

Descriptive statistics

Standard deviation10.090204
Coefficient of variation (CV)0.45232957
Kurtosis-1.2455687
Mean22.307195
Median Absolute Deviation (MAD)7.515
Skewness0.36044568
Sum17131.926
Variance101.81222
MonotonicityNot monotonic
2024-09-10T20:05:16.959160image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.16 6
 
0.8%
13 5
 
0.7%
15.23 4
 
0.5%
28.15 4
 
0.5%
14.6 4
 
0.5%
32.31 4
 
0.5%
10.68 4
 
0.5%
15.55 4
 
0.5%
15.09 4
 
0.5%
12.93 4
 
0.5%
Other values (577) 725
94.4%
ValueCountFrequency (%)
6.01 1
0.1%
6.04 1
0.1%
6.05 1
0.1%
6.07 1
0.1%
6.366 1
0.1%
6.37 1
0.1%
6.4 2
0.3%
6.77 1
0.1%
6.79 1
0.1%
6.81 1
0.1%
ValueCountFrequency (%)
43.1 1
0.1%
42.96 1
0.1%
42.77 1
0.1%
42.74 1
0.1%
42.62 1
0.1%
42.5 1
0.1%
42.49 1
0.1%
42.11 1
0.1%
42.08 1
0.1%
41.96 1
0.1%

Cooling_Load
Real number (ℝ)

HIGH CORRELATION 

Distinct636
Distinct (%)82.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.58776
Minimum10.9
Maximum48.03
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2024-09-10T20:05:17.317935image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum10.9
5-th percentile13.6175
Q115.62
median22.08
Q333.1325
95-th percentile40.037
Maximum48.03
Range37.13
Interquartile range (IQR)17.5125

Descriptive statistics

Standard deviation9.5133056
Coefficient of variation (CV)0.38691224
Kurtosis-1.1471903
Mean24.58776
Median Absolute Deviation (MAD)7.54
Skewness0.39599247
Sum18883.4
Variance90.502983
MonotonicityNot monotonic
2024-09-10T20:05:17.735627image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.33 4
 
0.5%
29.79 4
 
0.5%
14.27 4
 
0.5%
17.2 4
 
0.5%
14.28 4
 
0.5%
13.65 3
 
0.4%
14.61 3
 
0.4%
16.9 3
 
0.4%
13.79 3
 
0.4%
15.44 3
 
0.4%
Other values (626) 733
95.4%
ValueCountFrequency (%)
10.9 1
0.1%
10.94 1
0.1%
11.17 1
0.1%
11.19 1
0.1%
11.27 1
0.1%
11.29 1
0.1%
11.67 1
0.1%
11.72 1
0.1%
11.73 1
0.1%
11.74 1
0.1%
ValueCountFrequency (%)
48.03 1
0.1%
47.59 1
0.1%
47.01 1
0.1%
46.94 1
0.1%
46.44 1
0.1%
46.23 1
0.1%
45.97 1
0.1%
45.59 1
0.1%
45.52 1
0.1%
45.48 1
0.1%

Interactions

2024-09-10T20:05:08.369161image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T20:04:59.806834image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T20:05:01.478998image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T20:05:03.426744image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T20:05:05.057785image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T20:05:06.667919image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T20:05:08.636395image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T20:05:00.090905image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T20:05:01.781049image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T20:05:03.726569image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T20:05:05.337800image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T20:05:06.936710image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T20:05:08.937689image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T20:05:00.341278image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T20:05:02.106553image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T20:05:03.976799image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T20:05:05.603508image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T20:05:07.207665image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T20:05:09.265797image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T20:05:00.626546image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T20:05:02.438000image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T20:05:04.247946image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T20:05:05.865740image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T20:05:07.487743image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T20:05:09.566386image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T20:05:00.890908image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T20:05:02.707672image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T20:05:04.507875image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T20:05:06.105802image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T20:05:07.726626image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T20:05:09.878064image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T20:05:01.193466image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T20:05:03.158005image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T20:05:04.763743image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T20:05:06.367777image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T20:05:07.987684image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-09-10T20:05:17.980164image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Cooling_LoadGlazing_AreaGlazing_Area_DistributionHeating_LoadOrientationOverall_HeightRelative_CompactnessRoof_AreaSurface_AreaWall_Area
Cooling_Load1.0000.3590.0460.9730.0000.9630.6510.629-0.6510.416
Glazing_Area0.3591.0000.5730.5330.0000.0000.0000.0000.0000.000
Glazing_Area_Distribution0.0460.5731.0000.0680.0000.0000.0000.0000.0000.000
Heating_Load0.9730.5330.0681.0000.0000.9600.6220.623-0.6220.471
Orientation0.0000.0000.0000.0001.0000.0000.0000.0000.0000.000
Overall_Height0.9630.0000.0000.9600.0001.0000.9080.9990.9950.618
Relative_Compactness0.6510.0000.0000.6220.0000.9081.0000.939-1.000-0.256
Roof_Area0.6290.0000.0000.6230.0000.9990.9391.0000.9230.596
Surface_Area-0.6510.0000.000-0.6220.0000.995-1.0000.9231.0000.256
Wall_Area0.4160.0000.0000.4710.0000.618-0.2560.5960.2561.000

Missing values

2024-09-10T20:05:10.449964image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-10T20:05:10.973722image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Relative_CompactnessSurface_AreaWall_AreaRoof_AreaOverall_HeightOrientationGlazing_AreaGlazing_Area_DistributionHeating_LoadCooling_Load
00.98514.5294.0110.257.020.0015.5521.33
10.98514.5294.0110.257.030.0015.5521.33
20.98514.5294.0110.257.040.0015.5521.33
30.98514.5294.0110.257.050.0015.5521.33
40.90563.5318.5122.507.020.0020.8428.28
50.90563.5318.5122.507.030.0021.4625.38
60.90563.5318.5122.507.040.0020.7125.16
70.90563.5318.5122.507.050.0019.6829.60
80.86588.0294.0147.007.020.0019.5027.30
90.86588.0294.0147.007.030.0019.9521.97
Relative_CompactnessSurface_AreaWall_AreaRoof_AreaOverall_HeightOrientationGlazing_AreaGlazing_Area_DistributionHeating_LoadCooling_Load
7580.66759.5318.5220.53.540.4514.9217.55
7590.66759.5318.5220.53.550.4515.1618.06
7600.64784.0343.0220.53.520.4517.6920.82
7610.64784.0343.0220.53.530.4518.1920.21
7620.64784.0343.0220.53.540.4518.1620.71
7630.64784.0343.0220.53.550.4517.8821.40
7640.62808.5367.5220.53.520.4516.5416.88
7650.62808.5367.5220.53.530.4516.4417.11
7660.62808.5367.5220.53.540.4516.4816.61
7670.62808.5367.5220.53.550.4516.6416.03